Methods and systems for monitoring vehicle motion with driver safety alerts

ABSTRACT

Vehicle driver assistance and warning systems that alert a driver of a vehicle to wrong-way driving and/or imminent traffic control measures (TCMs) are disclosed. The system will identify a region of interest around the vehicle, access a vector map that includes the region of interest, and extract lane segment data associated with lane segments that are within the region of interest. The system will analyze the lane segment data and the vehicle&#39;s direction of travel to determine whether motion of the vehicle indicates that either: (a) the vehicle is traveling in a wrong-way direction for its lane; or (b) the vehicle is within a minimum stopping distance to an imminent TCM in its lane. When the system detects either condition, it will cause a driver warning system of the vehicle to output a driver alert.

BACKGROUND

A driver of a vehicle relies on signage painted on the roadways orsignposts on the side of the roadways to adapt the speed of the vehicleand steering to control the direction of travel along one-way roads,two-way roads, bridges, parking lots, and garages, for example. When adriver is driving along a road, the driver's attention may be divertedin the direction of moving or stationary objects, pedestrians and thelike. A driver's attention may be diverted when the driver is changingthe radio station or adjusting the vehicle's climate control. It is alsocommon for a driver to consume food and beverages, talk on the phone ortext while driving. All of these tasks can take the driver's attentionoff the road even if it is only for a second. As a consequence, certaintraffic control measures, such as traffic stop signs and a change incolor of a traffic light may go unseen. This can cause the driver tofail to stop at a stop sign or a red light, for example. Failing to stopat a stop sign, a red light or other traffic control measures increasesthe likelihood of a collision by the vehicle with another vehicle orpedestrian.

A driver can have their attention focused on the road 100% of the time.However, in some instances, traffic signs can be obscured from thedriver's view partially or completely, such as by trees or bushes, whichmay prevent the driver from stopping at the stop sign. In otherinstances, once the driver sees the traffic sign, it may be too late forthe driver to decelerate the vehicle at a rate sufficient to stop forthe traffic sign. A driver may not see a one-way sign blocked by treesor bushes while turning onto a road, which has an opposite heading thanthe direction of travel of the vehicle. Driving against traffic alsoincreases the likelihood of a collision by the vehicle with anothervehicle or pedestrian.

This document describes methods and systems that are directed toaddressing the problems described above, and/or other issues.

SUMMARY

In various scenarios, in a method of assisting a driver of a vehicle,one or more sensors of a vehicle will sense a direction of travel of thevehicle. A system that includes a computing device of the vehicle willinclude programming instructions that are configured to cause aprocessor of the device to identify a region of interest that comprisesan area that is in proximity to, and which includes, a current locationof the vehicle. The system will access a vector map that includes theregion of interest, and it will extract lane segment data associatedwith lane segments of the vector map that are within the region ofinterest. The system will analyze the lane segment data and thedirection of travel to determine whether motion of the vehicle satisfiesa condition associated with one or more of the following: (a) adirection of travel of a lane that corresponds to the current locationof the vehicle; or (b) a minimum stopping distance to an imminenttraffic control measure in the lane that corresponds to the currentlocation of the vehicle. Option (a) may correspond to a wrong-waydetection process, while option (b) may correspond to a traffic controlmeasure (TCM) location detection process. When the motion does notsatisfy the condition, the system will cause a driver warning system ofthe vehicle to output a driver alert of non-valid motion.

In the wrong way detection process, the lane segment data may include aheading for each lane segment in the region of interest. The headingwill correspond to the direction of travel of the lane that correspondsto the current location of the vehicle. The condition will be associatedwith the direction of travel of the lane that corresponds to the currentlocation of the vehicle. In this situation, when determining whether themotion of the automated vehicle satisfies the condition, the system may:(a) determine whether any of the lane segments within the region ofinterest has a heading that is within an opposing-heading tolerancerange and that is opposing the sensed direction of travel, (b) determinewhether any of the lane segments within the region of interest has aheading that is within a similar-heading tolerance range and that issimilar to the sensed direction of travel. The system will determinethat the motion does not satisfy the condition, and it will determinethat the sensed direction of travel is a wrong-way direction, when both:(i) at least one the lane segments is determined at (a) to have theheading opposing the sensed direction of travel; and (ii) none of thelane segments in the region of interest is determined at (b) have theheading that is similar to the sensed direction of travel. Otherwise,the system determining that the motion satisfies the condition.

In the TCM detection process, the lane segment data may include aheading and length for each lane segment. The condition may beassociated with the minimum stopping distance to an imminent TCM in thelane that corresponds to the current location of the vehicle. The stepof determining whether the motion of the vehicle satisfies the conditionmay include: (a) receiving, from one or more sensors of the vehicle, aspeed of travel of the vehicle; determining a minimum stopping distancerepresenting a distance from the current location of the vehicle and alocation within a stop zone that is within the lane of travel before thelocation of the TCM; and (c) using the speed and the current location todetermine whether the vehicle can stop within the minimum stoppingdistance.

Optionally, in the TCM detection process, when using the speed and thecurrent location to determine whether the vehicle can stop within theminimum stopping distance, the system may compute a rate of decelerationrequired to stop the vehicle at the location within the stop zone, andit may determine whether the computed rate of deceleration meets adeceleration threshold. if the computed rate of deceleration is over thedeceleration threshold, the system may determine that the motion doesnot satisfy the condition. Otherwise, the system may determine that themotion satisfies the condition.

In addition or alternatively, prior to determining the minimum stoppingdistance, the system may calculate a stopping zone polygon for the TCM,and it may calculate an end threshold polyline for the traffic controlmeasure that is at an end of the stopping zone polygon. The locationwithin the stop zone will correspond to the end threshold polyline, andthe minimum stopping distance will be determined through the stoppingzone polygon and up to the end threshold polyline. To calculate thestopping zone polygon comprises, the system may set a width of thestopping zone polygon to be larger than a width of the lane segment. Thesystem may then calculate the minimum stopping distance to include adistance of travel that includes a veering motion to an end of the widthof the stopping zone polygon.

In some embodiments, in the TCM detection process the system may detectmore than one candidate active TCM ahead of the vehicle in the directionof travel of the vehicle. The system also may detect that a trafficsignal activation command has been initiated in the vehicle before thevehicle reaches a first one of the candidate traffic control measure. Ifso, then it may determine which of the TCMs is relevant by ranking thecandidate TCMs based on distances from the vehicle to each of thecandidate TCMs or remaining lane segments in the region of interestbetween the vehicle and the candidate TCMs. In may then select theimminent TCM based on one or both of the distance and the vehicleturning signal state.

In other embodiments an automated vehicle's on-board computing systemmay determine whether the vehicle is moving in a wrong-way direction ina lane by: (a) calculating a location, orientation, pose of theautomated vehicle; (b) determining a direction of travel for theautomated vehicle based on the calculated location, orientation, pose ofthe automated vehicle; (c) identifying a region of interest thatincludes an area in an immediate vicinity of the automated vehicle; (d)accessing a vector map that includes the region of interest, andextracting from the vector map lane segments that are within the regionof interest from the vector map, wherein each of the lane segmentsincludes a heading that associates the lane segment with a lane'sdirection of travel; (e) determining whether any of the lane segmentswithin the region of interest has a heading within an opposing headingtolerance range opposing to the sensed direction of travel; (f)determining whether any of the lane segments within the region ofinterest near the location of the automated vehicle has a heading withina similar heading tolerance range that is similar to the senseddirection of travel; and (g) determining the automated vehicle has amotion that is a wrong-way direction when both: (i) at least one thelane segments is determined at (e) to have the heading opposing thesensed direction of travel, and (ii) none of the lane segments at (f)have the heading that is similar to the sensed direction of travel.

Optionally, in response to determining that the motion is the wrong-waydirection, the system may generate a control signal to alert a humanoperator that the direction of travel for the automated vehicle is thewrong-way direction. Further, in response to determining that the motionis the wrong-way direction, the system may log information associatedwith determining that the motion is the wrong-way direction.

Optionally, in various embodiments to identify the region of interestaround the vehicle, the system may determine a trajectory of thevehicle, identify a radius around the automated vehicle, and define theregion of interest as a circle in which the automated vehicle ispositioned at a center point of the circle.

When the vehicle is an automated vehicle, when determining that thevehicle is moving in a wrong-way direction the system may correct themotion of the automated vehicle. To do this, the vehicle's motioncontrol system may generate a control signal to control the vehicle'ssteering controller, speed controller and/or brake controller.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a flow chart of an example method for validatingmotion of a vehicle along a path based on high-definition map data.

FIG. 2 illustrates a flow chart of an example method for validatingheading motion of the vehicle.

FIG. 3 illustrates a flow chart of an example method for generating amotion correction.

FIGS. 4A-4C illustrate a flow chart of an example method for validatingmotion of a vehicle based on detected traffic control measures.

FIG. 5 illustrates an example scenario of a high-definition map of lanesegments traveled by the vehicle.

FIG. 6 illustrates an example circular region of interest with thevehicle in a center and overlaid on the map of lane segments of FIG. 5.

FIG. 7A illustrates an example scenario of a vehicle turning a wrong-wayonto a one-way intersecting street in the map of lane segments of FIG.5.

FIG. 7B illustrates an example angle between a vehicle heading and aheading of a lane segment.

FIG. 8 illustrates an example scenario of a vehicle turning onto atwo-way intersecting street in a high-definition map of lane segments.

FIG. 9 illustrates an example scenario of the vehicle veering around aparked car into a wrong-way lane.

FIG. 10 illustrates an example scenario of the vehicle navigating acrossa two-way road from outside of the vehicle lanes by making a left turnin a distal vehicle lane.

FIG. 11 illustrates an example scenario of the vehicle navigating acrossa two-way street from outside of the vehicle lanes by making a U-turn.

FIG. 12 illustrates another example scenario of the vehicle navigatingacross a two-way street from outside of the vehicle lanes by making aleft turn in a distal vehicle lane.

FIG. 13 illustrates an example scenario of a high-definition map of lanesegments with various traffic control measures indicated.

FIG. 14 illustrates an example scenario of the high-definition map oflane segments of FIG. 13 with computed stop zone polygons and polylinesoverlaid on the map.

FIG. 15 illustrates an example scenario of a high-definition map of lanesegments with a traffic control measure in advance of an intersectionbetween a one-way street and a two-way street.

FIG. 16 illustrates an example scenario of the high-definition map oflane segments of FIG. 13 with the distances to each of two trafficcontrol measures indicated.

FIG. 17 illustrates an example system architecture with an on-boardcomputing device for an automated vehicle.

DETAILED DESCRIPTION

As used in this document, the singular forms “a,” “an,” and “the”include plural references unless the context clearly dictates otherwise.Unless defined otherwise, all technical and scientific terms used hereinhave the same meanings as commonly understood by one of ordinary skillin the art. As used in this document, the term “comprising” means“including, but not limited to.”

As used in this disclosure, the term “wrong-way direction” is intendedto refer to an assigned heading of a lane segment that is opposite thecomputed heading or direction of travel of the vehicle within a presettolerance.

As used in this disclosure, the term traffic control measure (TCM)refers to an object that is installed along, in, or over a lane of aroad to control the flow of traffic by slowing and/or stopping thetraffic under certain conditions. Examples of TCMs include a stop sign,traffic light signal, a yield sign, a pedestrian crosswalk, and a speedbump.

As used in this disclosure, the term “vector map” refers to ahigh-definition set of digital map data that represents roads usingpolygons that are split-up into smaller chunks called “lane segments.”Each lane segment is represented by a polygon that has a lane centerlinewith at least one of a heading and a length. Each lane segment mayinclude a width.

In this document, the term “region of interest” (ROI) is an areadefining some or all vehicle lanes in proximity to a location of thevehicle. The ROI will include the vehicle and all lane segments that arepresent within a radius that extends from the vehicle. The radius may bea fixed value, or it may have a value that increases as vehicle speedincreases, and that decreases as vehicle speed decreases. The vehiclelanes include one or more of the following: (1) a travel lane with thesame heading that the vehicle is currently traveling; (2) a lane with anopposite heading of the vehicle and parallel and adjacent to the travellane; and/or (3) any intersecting lane within a predetermined number offeet of the current location of the vehicle. The ROI may place thevehicle at a center point of the area.

Definitions for additional terms that are relevant to this document areincluded at the end of this Detailed Description.

In an embodiment, an automated vehicle operating in an environment mayuse sensors to automatically identify the current speed and direction oftravel of the vehicle. When a driver is manually operating a fullyautomated vehicle in a manual mode, or when the driver is operating asemi-autonomous vehicle that includes driver assistance capabilities,the vehicle uses data from a high-definition map of the area to validateits motion so that the driver may be alerted if they are: (a) in dangerof failing to stop for a traffic control measure or (b) driving thewrong-way (i.e., against the heading of a lane segment).

In some embodiments, the data of the high-definition map may be used toalert the driver of driving the wrong-way down a one-way street, forexample. Driving the wrong-way can happen, for example, if the “one way”aspect of the street is poorly signposted or missing. This can alsohappen, for example, when a driver unknowingly drives onto interstatelanes in the opposing direction when it is late at night.

In some embodiments, the data of the high-definition map may be used toalert the driver of an out-of-limit deceleration rate to stop within thestopping distance to the next active traffic control measure.

The methods and systems will be described in relation to FIGS. 1-4C and17. The method steps may be performed in the order shown or a differentorder. One or more of the steps may be performed contemporaneously.Furthermore, one or more steps may be added or omitted in an iteration.

FIG. 1 illustrates a flow chart of an example method 100 for validatingmotion of a vehicle such as an automated vehicle 10 (FIG. 5) along apath based on high-definition map data 1771 (FIG. 17) associated with ahigh-definition map. In various embodiments, the high-definition map maybe a vector map.

At 102, the on-board computing device 1710 may identify a ROI in animmediate vicinity of a current location of the automated vehicle 10.The system may do this by retrieving global positioning system (GPS)data that includes coordinates of the current location, by receivingimages of the current location from one or more onboard cameras andprocessing the images to identify one or more landmarks having knowncoordinates, and/or other procedures. At 104, the on-board computingdevice 1710 may access a vector map that includes those lane segments inthe ROI and, at 106, extract lane segment data 1774 (FIG. 17) associatedwith the plurality of lane segments that are within the ROI. The vectormap may be stored onboard the vehicle in a memory, and the vector mapmay include coordinates that the vehicle uses to select the correctvector map that includes coordinates of the ROI.

At 108, the on-board computing device 1710 may determine a direction oftravel or heading computed for the automated vehicle 10 based on datareceived from one or more of the sensors 1735 (FIG. 17). The on-boardcomputing device 1710 may also determine the speed of the vehicle. Thedirection of travel may be based on a first tolerance. The firsttolerance is an angular tolerance, and in particular a maximum anglebetween the vehicle's direction of travel and a reference line of a laneor lane segment in which the vehicle traveling, within which the lane orlane segment may be generally considered to be going in the samedirection as the vehicle. The first tolerance may be relatively large,to avoid false positives in case the vehicle is veering or otherwiseangled within the lane but still generally heading in the correctdirection. For example, the first tolerance for the automated vehicle 10may include ±1.3 radians or approximately ±74.48° relative to the lane'sdirection of travel or heading of the vector map to validate thedirection of travel by the vehicle as the same direction or thecorrect-way. Although ±1.3 radians is used in this example, the firsttolerance may range between ±1.1-1.9 radians, by way of non-limitingexample. In addition, the system may also consider a distance-basedmeasurement to be a second tolerance that it uses to determine whetherthe sensed direction of travel of the vehicle is a direction that isother than the lane's direction of travel. The second tolerance measureshow far a vehicle may move away from the reference line before which thevehicle may be considered to be heading in direction other than that ofthe lane. The second tolerance also should be relatively large to avoidfalse positives, for example the approximate width of a typical lane ofthe lane's in the jurisdiction in which the lane is located (which inthe United States is currently 3.6 meters). The third tolerance is theconceptual inverse of the first tolerance in that it is a minimum anglebetween the vehicle's facing direction and the reference line, beyondwhich a lane's direction may be opposite that if the vehicle. This angleshould be relatively small to avoid false positives. For example, thethird tolerance may be approximately ±0.35 radians or 20.05°, or in arange between ±0.03-0.044. Other values are possible. The toleranceranges described herein may be compared with computed angles or radians,as shown in FIG. 7B, to determine a heading of the vehicle 10 relativeto a particular lane segment.

The system may then use these tolerances to assess whether to monitor ifthe vehicle is traveling in a correct direction or a wrong-way directionas follows: (1) First, the system may search for all lanes whosedirection is within the first tolerance of the vehicle's direction, andwhich are within a distance of the second tolerance of the vehicle. Ifany such lanes are found, then the vehicle is traveling in the samedirection as a nearby lane, so the system may not need to activate thedirection monitor. (In this case, the AV might have veered into anoncoming lane to pass an obstacle, for example). (2) If no such lanesare found, then the system may search for all lanes whose direction iswithin the third tolerance of the opposite of the vehicle's direction,and which are within a small threshold distance of the AV (e.g. within avehicle-length diameter circle centered on the vehicle). If no suchlanes are found in the second step, then the system may determine thatthe vehicle is not near an opposite-direction lane, so it may notactivate the monitor. (In this instance, the vehicle may be in anunmapped parking lot, for example.) If the system finds nosame-direction lanes nearby in step (1), and it finds an oppositedirection lane nearby in step (2), then the system may determine thatthe vehicle is moving in the wrong direction for its lane, so the systemwill activate the monitor and/or trigger an alert.

At 110, the on-board computing device 1710 may monitor the motion of thevehicle 10 based on the lane segment data 1774 (FIG. 17). The motion maybe based on the one or more sensors 1735 (FIG. 17) relative to a plannedpath 1772 stored in memory 1770, for example. The system 1700 (FIG. 17)includes various sensors 1735 for collecting real-time data associatedwith the automated vehicle to which the sensors 1735 are attached. Thestate of operation of the automated vehicle 10 includes, among otherthings, the current speed and the current direction of travel, forexample, based on sensor data from the speed sensor 1748 (FIG. 17) andthe location sensor 1744 (FIG. 17). The state of the automated vehicle10 may include alignment of the vehicle body with a lane (i.e., theheading/orientation with respect to a lane segment), direction oftravel, speed and/or acceleration, and heading and/or orientation.

At 112, the on-board computing device 1710 may validate the motion ofthe vehicle 10 by determining whether the vehicle's motion satisfies acondition, and in particular determining whether the vehicle's directionof travel corresponds to the direction of travel of the lane in whichthe vehicle is currently located. When validating the motion of thevehicle 10, the on-board computing device 1710 may use the lane segmentdata of the high-definition map data for any instantiation. For example,the on-board computing device 1710 may validate the motion of thevehicle by determining whether the vehicle's current lane headingchanged (that is, whether the vehicle has moved into a lane or lanesegment having a different heading), at 114, as will be described inrelation to FIGS. 7A and 8. As the vehicle changes lane segments alongthe path, the current heading of the lane segment is retrieved from thelane segment data 1774.

If the determination, at 114, is “YES,” an assessment of the directionof travel of the vehicle is performed, at 116. By way of non-limitingexample, the assessment of the sensed direction of travel of the vehiclemay perform a false positive analysis, as will be described in relationto FIG. 2. Depending on the assessment, as will be described in relationto FIG. 2, the driver may be alerted, at 128.

In some embodiments, at 122, the on-board computing device 1710 may makea determination of whether a correction is needed based on theassessment at 116. The decision shape of 122 is shown in dashed lines todenote that it is optional. If the determination at either 114 or 122 is“NO,” the method may return to 102. For example, if the automatedvehicle 10 is fully autonomous, there may be no need for an alert.Instead, the on-board computing device 1710 may determine a motioncorrection, for example, at 123. If the automated vehicle 10 issemi-autonomous, there may be a safety driver alert and, in someembodiments, the on-board computing device 1710 may determine a motioncorrection action that it causes a motion control system of the vehicleto take an action such as decelerating, stopping, and/or veering into adifferent lane segment.

By way of non-limiting example, the on-board computing device 1710 mayvalidate the motion of the vehicle 10 by detecting whether there is animminent traffic control measure, at 118, ahead of the vehicle. Thesystem may detect whether an imminent traffic control measure is aheadby determining whether a measure present in the high-definition map datafor any instantiation. Alternatively and/or in addition, the on-boardcomputing device 1710 may receive notice of the traffic control measurevia a vehicle-to-infrastructure (V2I) or other vehicle-to-everything(V2X)-type communication in which the TCM emits signals to oncomingvehicles to alert the vehicle of its presence and status. In the case ofa traffic light, the vehicle also may determine whether the TCM isactively signaling the vehicle to slow or stop (such as a red or yellowtraffic light), or whether the TCM will do so when it reaches the TCM,based on a status indicator included in a V2X communication, or by usingonboard cameras to detect and determine a state of the light.

For example, at 118, the on-board computing device 1710 may validate themotion of the vehicle 10 by detecting whether there is a traffic controlmeasure that is imminent in that it is (a) ahead of the vehicle, (b) inor associated with the vehicle's current lane, and (c) within the ROI orwithin a threshold distance from the ROI. If the determination is “NO,”the method 100 may return to 102. On the other hand, if thedetermination is “YES,” the on-board computing device 1710 may assess aminimum stopping distance as a distance from the vehicle's currentlocation to a location in a stop zone that is ahead of (that is, before)the traffic control measure in front of the vehicle, at 120. In variousembodiments, the assessment may determine that the vehicle 10 istraveling at a speed and/or decelerating at a rate that is withindecelerating limits to stop for an imminent traffic control measure. Aswill be described in relation to FIGS. 4A-4C, if the motion of thevehicle finds that the decelerating rate necessary to stop for theimminent traffic control measure is out-of-limits, the driver isalerted, at 128 via an audible and/or visual alert. In some embodiments,at 120, the on-board computing device 1710 may log certain events orinformation related to traffic control measures, at 130, as described inrelation to FIG. 4C.

In some embodiments, at 122, the on-board computing device 1710 may makea determination of whether a correction is needed to slow the vehicle.If the determination at either 118 or 122 is “NO,” the method may returnto 102. On the other hand, if the determination at 122 is “YES,” then,at 123, the on-board computing device 1710 may perform a motioncorrection process.

The on-board computing device 1710 may perform a motion correctionprocess by determining a particular motion correction, at 124, based onthe assessments at 116 or 120, and cause the vehicle to perform thedetermined motion correction, at 126. By way of non-limiting example, ifcorrection is needed in response to the assessment, at 120, the on-boardcomputing device 1710 may determine that the vehicle 10 needs to slowdown to adjust the stopping distance to the next traffic controlmeasure. A correction may require the on-board computing device 1710 ofthe system 1700 to generate a speed control signal that is sent to thespeed controller 1728 (FIG. 17) to change the speed of the vehicle. Insome embodiments, the control signal may generate a braking controlsignal that is sent to the brake controller 1723 (FIG. 17) to reduce thespeed. In some embodiments, the control signal may generate a steeringcontrol signal that is sent to the steering controller 1724 (FIG. 17).

In various scenarios, in response to determining that the direction oftravel for the automated vehicle 10 is the wrong-way, a potentialcorrection motion may include preventing the vehicle 10 from performinga veering motion into an adjacent lane having an opposing heading thanthe heading of the vehicle 10, at 126. Thus, the on-board computingdevice 1710 may generate a control signal that prevents the automatedvehicle 10 from executing the veer into the adjacent lane. The controlsignal may include a steering control signal that is sent to thesteering controller 1724 to prevent the steering of the vehicle toperform a “veering” motion. It should be understood, that depending onthe scenario other corrective motions may be performed.

At 128, the on-board computing device 1710 may cause a driver warningsystem 1780 (FIG. 17) to provide an appropriate driver alert. Forexample, the driver warning system 1780 may include an audio speaker1782 (FIG. 17), a display 1784 (FIG. 17), light indicator 1786 (FIG. 17)and/or vibration device 1788 (FIG. 17), any or all of which may beactivated to output the alert. The alert may be audible through speaker1782, an illumination of a designated light indicator 1786, anotification message or light on a display 1784 and/or vibrationfeedback via a vibration device 1788. The display may be a heads-updisplay. The vibration device may be integrated into a part of thevehicle, such as a driver seat, steering wheel, etc. By way ofnon-limiting example, if the vehicle 10 is detected driving thewrong-way, at 114, an audible alert may be generated to inform a humanoperator that the direction of travel or heading of the automatedvehicle 10 is a wrong-way. The event data or information used in thewrong-way detection may be logged, at 130, so that a report can begenerated for later analysis. For example, the signpost for the road maybe missing. The logged data may be used to update a variety of vehiclesincluding a fully autonomous vehicle using a computer vision system toidentify signs along the road. In various embodiments, the “wrong-way”alert may be configured to repeat after a time period delay to avoidgenerating multiple alerts for the same incident. This may allow thevehicle 10 and/or human operator operating the vehicle to takecorrective action. The alert may include one or more alert modessimultaneously. For example, driver warning system 1780 may generate analert using one or more of the speaker 1782 (FIG. 17), the display 1784(FIG. 17), light indicator 1786 (FIG. 17) and/or vibration device 1788(FIG. 17), simultaneously.

For illustrative purposes and understanding, FIG. 5 is an examplescenario of a high-definition map 500 of lane segments traveled by thevehicle 10, described above, at 102 and 104. The vehicle 10 isrepresented with an interior triangle. The apex of the triangle pointsin the direction of travel of the vehicle. Each of the lane segments 503may be represented as a polygon that includes a heading 504, denoted bya first arrow, to associate the lane segment 503 with a lane's directionof travel. The map 500 also illustrates adjacent lane segments 513 thatmay be represented as a polygon having a heading 514, denoted by asecond arrow, to associate the lane segment 513 with its lane'sdirection of travel. The headings 514 are in a different direction thanheadings 504 and are anti-parallel (i.e., the lane segments are parallelbut vehicles within them will move in opposite directions). The map 500may also include at least one lane segment 523 intersecting the lanesegments 503. The at least one segment 523 may be represented as apolygon that includes a heading 524, denoted by a third arrow, whichintersects heading 504 and 514. For example, lane segment 523 intersectslane 503 at approximately 90°. However, other intersecting lane segmentsmay intersect at a different angle and this document should not belimited in any way to 90° intersections. In this example, the vehicle 10is currently traveling in a direction of heading 504 of lane 503.

For the sake of illustration and understanding, FIG. 6 illustrates anexample scenario of a circular ROI 600 with the vehicle 10 in a centerof the ROI and overlaid within the map 500 of the lane segments. Anylane segment touched or enclosed by the ROI is considered “in proximity”to the vehicle 10. Other lane segments in the map vector 500 may beignored by the on-board computing device 1710, in some embodiments. TheROI 600 may be a geometric-shaped area surrounding the vehicle 10. Invarious embodiments, the ROI 600 may have a particular radius to aboundary of the area and with the vehicle 10 located at or near a centerof the ROI 600. It should be understood, that the vehicle 10 does nothave to be located at a center of a ROI.

Wrong-Way Direction

FIG. 2 illustrates a flow chart of an example method 200 for assessingthe direction of travel, at 116, of FIG. 1 to validate the headingmotion of the vehicle 10. FIG. 2 will be described in relation toexample scenarios depicted in FIGS. 7A and 8.

Returning again to FIG. 2, the method 200 for assessing the direction oftravel may assess whether the vehicle is driving in a wrong-waydirection. The method 200 may check for false positives such as, withoutlimitation, whether the vehicle 10 is i) driving onto a two-way road, ora parking lot or other area with no assigned direction (FIG. 8); ii)driving in a center turn lane; or iii) veering into an oncoming lane(i.e., a lane with a direction of travel that is opposite that of thevehicle's) to pass a parked car 20 (FIG. 9). The condition at thevehicle's direction of travel must satisfy may require that there be nosuch false positives. At 202, the on-board computing device 1710 maydetermine whether there is a lane segment, in proximity to the vehicle10, that has a similar heading to the opposing heading (within thesecond tolerance) of the vehicle 10. If the determination at 202 is“YES,” the on-board computing device 1710 may determine whether there isno lane segment near the vehicle's position with a similar heading(within the third tolerance) to the vehicle 10, at 204.

In various embodiments, only lane segments where a car or truck iscapable of driving should be considered in the list of lane segments ofthe ROI. In other words, bike lanes, parking lanes, and bus lanesoptionally may not be considered in this process. Both criteria at 202and 204 may be checked to avoid false positives when the vehicle 10 isdriving on a two-way road, driving in a center turn lane (where thereare lanes both matching and opposing the vehicle's heading overlaid onone another), or when the vehicle 10 veers into the oncoming lane topass a parked car, as illustrated in FIGS. 9-12. Thus, at 204, if thedetermination is “NO,” the on-board computing device 1710 determinesthat the vehicle 10 is traveling in the direction of the correct-way, at206. The assessment may determine that the motion of the vehicle isvalid, at 206. By way of non-limiting example, an alert is not generatedwhen the vehicle is performing any of the maneuvers of FIGS. 9-12because there is a lane segment near the vehicle's position with asimilar heading as the sensed direction of travel of the vehicle. Inaddition, the vehicle is able to drive across lanes with headingsdetermined to be at an angle of intersection for the road geometryrelative to the sensed direction of travel without a need for an alert.

Specifically, depending on the situations, if the determination ateither of 202 and 204 is “NO,” the on-board computing device 1710 maydetermine that the motion (i.e., direction of travel) is the correct-wayor valid. If the determination at 204 is “YES,” the on-board computingdevice 1710 may detect that the vehicle is traveling in a direction thatis a wrong-way, at 208, so that an alarm may be generated, at 128, and,in some embodiments, a corrective action taken as determined, at 122. At208, the direction of travel by the vehicle is determined to benon-valid based on the high-definition map data and the presettolerances.

In various scenarios, the preset tolerance used to determine “similarheading” may be small to avoid registering false positives. Examplescenarios relate to when the vehicle 10 is preparing to enter a lanesegment from outside any lanes (e.g., at the start of a predictedveering or turning motion, when the vehicle 10 is within the drivablearea but not within a lane segment), as will be described in relation toFIGS. 9-12. Based on the heading data, the on-board computing device1710 may determine which lane segments are pointed in the same directionas the vehicle 10, and which are not.

One-Way Lane

For illustrative purposes, FIG. 7A illustrates an example scenario of avehicle turning onto a wrong-way intersecting street in the map of lanesegments. The ROI 600 (FIG. 6) may be updated, by the on-board computingdevice 1710, as the vehicle travels. For example, as the vehicle 10travels along the lane segments of the map, those lane segments of theprior ROI 600 that are no longer in proximity of the vehicle 10 areignored or removed from a list of lane segments. The on-board computingdevice 1710 may select lane segments, if they are within a particularradius surrounding the vehicle 10.

In operation, the on-board computing device 1710 may access thehigh-definition map data 1771 of high-definition map and select the ROIlane segment data 1774 that is representative of an example list 730.Assume for the illustration of FIG. 7A, a list 730 of those lane segmentdata in the ROI 700 include lane segments LS1-LS8. The list 730 includesat least the heading data for each lane in the current ROI 700. The lanesegments LS4-LS6 have a heading that corresponds to the heading of thevehicle 10 and corresponds to heading H1. Heading H1 is an East heading,as the arrow is directed to the right of the page. Lane segments LS1-LS3are adjacent and parallel to lane segments LS4-LS6 but have a heading H2that is in an opposite direction of lane segments LS4-LS6 and thevehicle 10. Heading H3 is a West heading, as the arrow is directed tothe left of the page. Lane segments LS7-LS8 have a heading H3 thatintersects heading H1, for example. Heading H3 is a North heading, asthe arrow is directed toward the top of the page. In the example of FIG.7A, assume the vehicle 10 turns onto lane segment LS7. The motion of thevehicle may be based on the current state of the vehicle sensed bysensors 1735 (FIG. 17) such as a location sensor 1760. A first toleranceas described may be compared with angle α2 to determine the currentvehicle heading relative to the lane segment heading (i.e., heading H3)to assess “opposing” headings before a full and after turn is made, suchas shown in FIG. 7B. Using the frame of reference in FIG. 7B, the angleα2 is greater than 180°. Since the angle α2 is outside of the firsttolerance (i.e., the angle beyond which the lane heading is consideredto be opposite that of the vehicle's direction), an alert would begenerated. After the full turn is made, the vehicle 10 may have an angleα2 of approximately 180°, which is in the first tolerance range.

In FIG. 7A, the vehicle 10 has part of the vehicle's body in lanesegment LS5 and part encroaching into lane segment LS7. FIG. 7Billustrates an example angle α2 between a vehicle heading and a headingof a lane segment LS7. An example angle α2 is denoted as the anglebetween the computed heading HV of the vehicle 10 relative to heading H3from list 730. Thus, at 202, the on-board computing device 1710 wouldretrieve the ROI lane segment data of list 730 to determine that thevehicle 10 is turning onto lane segment LS7 and its heading H3 that is aNorth direction. However, based on the sensors 1735, the on-boardcomputing device 1710 may determine that the heading of the vehicle 10is heading generally South, which is opposite that of heading H3 forlane segment LS7 based on some preset tolerance, such as the secondtolerance.

Two-Way Lanes

FIG. 8 illustrates an example scenario of a vehicle turning onto atwo-way intersecting street in a high-definition map of lane segments.In this scenario, the vehicle 10 is turning onto lane segment LS9 with aheading H4 that is a South direction. Accordingly, at 202, the on-boardcomputing device 1710 may retrieve the ROI lane segment data of list 830to determine that the vehicle 10 to turn onto lane segment LS9 and itsheading H4. The list 830 is similar to list 730 but also includes lanesegments LS9-LS10 and their headings H4.

In the scenario of FIG. 8, at 202, the on-board computing device 1710may also determine, from the ROI lane segment data of list 830, thepresence of the lane segments LS7 and LS8 with heading H3, based ontheir proximity to lane segment LS9, for example. In this example, thedetermination, at 202, is “YES” because the heading of lane segments LS7and LS8 opposes the heading of the vehicle.

Based on the sensors 1735, the on-board computing device 1710 maydetermine that the heading of the vehicle 10 is the same as that ofheading H4 for lane segment LS9 within some preset tolerance (i.e., thethird tolerance). Hence, at 204, the on-board computing device 1710determines that heading H4 for lane segment H9 is a similar headingwithin the third tolerance.

Additional scenarios will be described in relation to FIGS. 9-12. Ineach of the scenarios 900, 1000, 1100 and 1200, the street has a lanesegment LS4, which is in closest proximity to vehicle 10. Thus, the lanesegment LS4 may sometimes be referred to as a “proximal lane segment.”In each of these examples, the street has a lane segment LS1, which isin farthest from the vehicle 10. Thus, the lane segment LS1 maysometimes be referred to as a “distal lane segment.” The examplescenarios 1000, 1100 and 1200 show example trajectories the vehicletravels to cross the lane segments of a two-way road at various anglesrelative to the headings of the lane segment that is not within thesecond tolerance range, accordingly, an alert is not generated. Fordiscussion purposes, assume that the driver's motion in any of thescenarios of FIGS. 9-12 are examples of valid motions.

FIG. 9 illustrates an example scenario 900 of the vehicle 10 veeringaround a parked car 20 into a permitted wrong-way lane segment LS1. Inthe scenario 900, assume that the vehicle 10 is driving in lane segmentLS4 with a parked vehicle 20 ahead of the vehicle 10. The human operatorof the vehicle 10 may use lane segment LS1 to go around the parkedvehicle 20 by veering into the lane segment LS1 and traveling along atrajectory 910. The trajectory 910 returns the vehicle to lane segmentLS4. However, lane segment LS1 has a heading that is opposing that ofthe vehicle's heading. If the human operator veers into the lane segmentLS1, such motions will be detected as a correct-way because lane segmentLS4 is in proximity to the vehicle. Hence, the determination, at 204,would be “NO.”

FIG. 10 illustrates an example scenario 1000 of the vehicle 10navigating across a two-way road from outside of the vehicle lanesegments LS1 and LS4 by intersecting lane segments at an intersectingangle and making a left turn in a distal vehicle lane segment LS1. InFIG. 10, the sensed direction of travel of the vehicle is not within thesecond tolerance range. Hence, the determination, at 202, would be “NO.”Alternately, once the vehicle reaches lane segment LS1, the lane segmentis within the third tolerance range. Hence, the determination, at 204,would be “NO.” Accordingly, using the method 200 of FIG. 2, an alert isnot generated since the motion is valid.

FIG. 11 illustrates an example scenario 1100 of the vehicle 10navigating across a two-way street from outside of the vehicle lanes bymaking a U-turn onto distal lane segment LS1. In FIG. 11, the vehicle 10has the same direction of heading as lane segment LS4. Then, thevehicle's motion proceeds to cross lane segment LS4 to lane segment LS1at an intersection angle not within the second and third toleranceranges. Hence, the determination, at 202, would be “NO.” Alternately,once the vehicle reaches lane segment LS1, the lane segment is withinthe third tolerance range. Hence, the determination, at 204, would be“NO.” Accordingly, using the method 200 of FIG. 2, an alert is notgenerated since the motion is valid.

FIG. 12 illustrates another example scenario 1200 of the vehicle 10navigating across a two-way street from outside of the vehicle lanes bymaking a left turn in a distal vehicle lane segment LS1. Initially, thevehicle 10 has a direction that is opposite that of lane segment LS4.However, lane segment LS1 is present with a direction corresponding tothe same direction as the vehicle. As the vehicle 10 proceeds across thelane segments, it travels at an intersecting angle that is not withinthe second tolerance range and then makes a left turn onto lane segmentLS1. Hence, the determination, at 202, would be “NO.” Alternately, oncethe vehicle reaches lane segment LS1, the lane segment is within thethird tolerance range. Hence, the determination, at 204, would be “NO.”Accordingly, using the method 200 of FIG. 2, an alert is not generatedsince the motion is valid.

In the examples scenarios 1000, 1100 and 1200, the vehicle 10 in each ofthese scenarios is outside of the lane segments of the map. For example,the vehicle 10 could be in a parking lane, grass, emergency lane, or thelike, which is not generally marked for vehicle travel. It should beunderstood, that these example scenarios are for illustrative purposesand should not be limiting in any way.

Wrong-Way Path Correction

FIG. 3 illustrates a flow chart of an example method 300 for generatingwrong-way motion correction. In general, the method 300 incorporates theactivities at 124 and 126 of FIG. 1 related to the wrong-way pathcorrection. At 302, the on-board computing device 1710 may determine, inthe ROI, a planned trajectory of the vehicle 10. The path trajectory, at302, corresponds to the predicted future states of the automated vehicle10 including, without limitation, for a particular vehicle's speed,direction, pose and location. Each location of the trajectory may bestored in a data store or memory 1770. At 304, the on-board computingdevice 1710 may identify a correction lane segment or maneuver thatcorrects the direction of travel to be the correct-way. Specifically,the activities, at 124 of FIG. 1, correspond to those activities of 302and 304.

The on-board computing device 1710 may, in response to determining thatthe direction of travel for the vehicle is a wrong-way, prevent thevehicle 10 from executing the turn into the intersecting street (i.e.,lane segment LS7) or an adjacent lane segment with an opposing heading.This may include a turn into an intersecting street that intersects astreet in which the vehicle is currently traveling. This may alsoinclude identifying a lane segment of the intersecting street into whichthe automated vehicle will enter when the automated vehicle turns intothe intersecting street. At 306, the on-board computing device 1710 maycause the motion of the vehicle to perform a wrong-way correction usinga lane segment in the ROI. It should be understood that the operation at306 corresponds to example activities, at 126 of FIG. 1.

At 306, the on-board computing device 1710 may generate a control signalto control at least one of a steering controller 1724, a speedcontroller 1728 and a brake controller 1723 of the vehicle 10.

The system 1700 may define the ROI as a circle in which the vehicle ispositioned at a center point of the circle. In the example of FIG. 7A,if the vehicle was traveling in lane segment LS5, for example, butdriving in an opposite direction than heading H3, the vehicle may becontrolled to change lanes to lane segment LS2, for example.

Traffic Control Measure (TCM)

FIG. 4A-4C illustrates a flow chart of an example method 400 forvalidating motion of a vehicle based on detected traffic controlmeasures. The method 400 corresponds to activities of 120 of FIG. 1.Referring now to FIG. 4A, the on-board computing device 1710 mayidentify traffic control measures, at 402, which the vehicle 10 muststop in a ROI and specifically the area of the ROI that is forward ofthe vehicle. The traffic control measures may be retrieved from the dataof the vector map associated with the ROI. The traffic control measuresmay be associated with a lane segment, for example. In some embodiments,the start of a lane segment or an end of a lane segment may beassociated with a traffic control measure. The on-board computing device1710 may compute a stop zone polygon, at 404, and an end thresholdpolyline, at 406, as will be described later in relation to FIGS. 13-14.

At 408, the on-board computing device 1710 may determine whether thetraffic control measure has a painted stop line. If the determination,at 408, is “NO,” the on-board computing device 1710 may adjust thelocation of the stop zone polygon and end threshold to be before theintersection, in various embodiments. At 410, for a stop sign with nopainted stop line, rather than requiring the vehicle to stop at the stopsign, it may be preferred to require it to stop before entering otherlanes of traffic, as will be described in relation to FIG. 15. Thismight be preferred as in some cities stop signs with no painted linesare placed very far from the intersection. Accordingly, the placement ofthe stop zone polygon and end threshold may vary based on geographicallocation data such as for cities, neighborhoods or other jurisdictionswith unique painted stop line requirements.

At 408, if the determination is “YES,” or after 410, the method 400 mayproceed to FIG. 4B. Before describing FIG. 4B, the example scenarios oftraffic control measures will be described in relation to the examplesof FIGS. 13-15.

FIG. 13 illustrates an example scenario of a high-definition map 1300 oflane segments with various traffic control measures 1310 and 1320indicated in ROI 1301. FIG. 14 illustrates an example scenario 1400 ofthe high-definition map of lane segments of FIG. 13 with computed stopzone polygons 1410 and 1420 and end threshold polylines 1415 and 1425overlaid on the map 1300 (FIG. 13). To prevent crowding of the figure,the ROI has been omitted from FIG. 14. As shown, the vehicle 10 istraveling along a road with lane segments LS11-LS15. The map 1300 alsoincludes lane segments LS16-LS17. The map 1300 may include trafficcontrol measure 1310 that is a traffic light (TL). The traffic controlmeasure 1310 includes a red light 1312, a yellow light 1314 and a greenlight 1316. In the illustration, only one light is illuminated at a timefor the sake of discussion. In this instance, assume that the red light1312 is illuminated, as denoted by the dotted hatching of the light1312. The vehicle may determine that the light is red by processingimages of the light that the vehicle captures via on-board cameras,and/or by V2X communications that report the light's state via anexternal system of which the traffic light is a part. The map alsoincludes traffic control measure 1320 that is a stop sign. The on-boardcomputing device 1710 may use the lane segment data of thehigh-definition map data 1771 to determine whether the vehicle 10 canstop in time for the traffic control measures that are ahead of thevehicle in the direction of travel. Some of those traffic controlmeasures can be on an intersecting street to the current path of thevehicle.

In the illustration of FIG. 14, a traffic control measure 1310 ispositioned essentially at the end of lane segment LS14 or the beginningof lane segment LS15, as an example. In FIG. 14, an example list 1430 oflane segment data for the ROI ahead of the vehicle is shown. The stopzone polygon 1410 is shown overlaid on top of the end of lane segmentLS14 or the beginning of lane segment LS15 with a dotted hatching. Theend threshold polyline 1415 is denoted in diagonal line hatching, whichbegins where the stop zone polygon ends and is extended a distance intothe next lane segment (i.e., lane segment LS15). The dividing line 1413is a demarcation point between lane segment LS14 and LS15 with a headingH1. The heading H1 is represented as an East heading with lane segmentLS15 represented as East of lane segment LS14. Each lane segment isassociated with a length L1. In the list of 1430, “TCM-TL” representsdata entry associated with a traffic light type of traffic controlmeasure. In the list 1430, the “TCM-STOP” represents data entryassociated a stop-sign-type traffic control measure. Although not shown,the ROI lane segment data in list 1430 may include data associated witha width of the polygon of each lane segment.

In the illustration, a traffic control measure 1320 is positionedessentially at the end of lane segment LS16 or the beginning of lanesegment LS17. The stop zone polygon 1420 is shown overlaid on top of theend of lane segment LS16 or the beginning of lane segment LS17 with adotted hatching. The end threshold polyline 1425 is denoted in diagonalline hatching, which begins where the stop zone polygon ends and isextended a distance into the next lane segment (i.e., lane segmentLS17). The dividing line 1423 is a demarcation point between lanesegment LS16 and LS17 with a heading H2. The heading H2 is representedas a North heading with lane segment LS17 represented as North of lanesegment LS16.

It should be understood, that the end threshold polyline may bepositioned at the stop line, or slightly past it, if it is acceptablefor the vehicle to stop a little over the stop line. It may extend farto the left and right of the lane segment, in case the driver of thevehicle 10 needs to veer out of lane to the left or right (for example,to avoid an obstruction) to navigate the road. The vehicle 10 veeringinto the expanded region of the stop zone and the polyline may be anexample of normal driving motion of a driver. It should be understood,the stop zone polygon 1420 may be computed by the on-board computingdevice 1710 to generate a polygon to extend some distance backward fromthe end threshold polyline 1425, covering the area in which the vehiclemust stop.

FIG. 15 illustrates an example scenario 1500 of a map of lane segmentswith a traffic control measures 1520 in advance of an intersection of aone-way road and a two-way road. For a stop sign with no painted stopline, rather than requiring the vehicle 10 to stop at the stop sign, itmay be preferred to require it to stop before entering other lanes oftraffic.

The scenario 1500 may include lane segments LS21-LS23, which arerepresented as one-way, heading East. The scenario 1500 may includetraffic control measure 1520 that is a stop sign. The scenario 1500 mayinclude lane segments LS24-LS25 that intersect lane segment LS23. Thelane segments LS24-LS25 have a South heading. The scenario 1500 mayinclude lane segments LS26-LS27 that intersect lane segment LS23 and areparallel to lane segments LS24-LS25. The lane segments LS26-LS26 have aNorth heading. In the example, the lengths of the lane segmentsLS22-LS23 have lengths that are different from lengths of other lanesegments in FIG. 15.

In the scenario 1500, the traffic control measure 1520 may be positionedbetween lane segments LS22 and LS23. However, the stop zone polygons1521 and end threshold polylines 1525 are located at the intersection oflane segments LS23 and lane segments LS24-LS25, corresponding toactivities at 410 of FIG. 4A.

Returning now to FIG. 4B, at 432, the on-board computing device 1710 maycompute the minimum stopping distance at the vehicle's current speed and(reasonable) deceleration/jerk rate to a traffic control measure, suchas traffic control measures 1310 and 1320 of FIG. 13. Exampledeceleration/jerk rates are described later. An example algorithm fordetermining minimum stopping distance is d=v²/2a, in which v=thevehicle's velocity and a=the vehicle's acceleration.

The minimum stopping distance may also be based on ambient environmentalconditions as determined by the environmental sensors 1768. For example,in the presence of rain, snow, or surface type may vary the frictioncoefficient. The stopping distance may be computed by calculating areaction time and a braking time. The minimum stopping distance is theshortest stopping distance for a particular deceleration rate.

At 434, the on-board computing device 1710 may identify, from thehigh-definition map data, the lane segments in the map in front of thevehicle 10. The lane segments in front of the vehicle 10 may correspondto those lane segments, which are in the direction of travel or have thesame heading as the vehicle 10. These lane segments may be part of theROI but only those lane segments, which are ahead of the vehicle 10. Theon-board computing device 1710 may retrieve the ROI lane segment data1774. The ROI lane segment data 1774 may include the length of each ofthe lane segments in front of the vehicle 10. To simplify thediscussion, assume that all the lane segments have the same length L1,as shown in FIG. 14. It should be understood, that the lane segments mayhave different lengths, as shown in FIG. 15.

At 436, the on-board computing device 1710 may search for all the activetraffic control measures (stop signs/red or yellow lights) for lanesegments ahead of the vehicle 10, within the minimum stopping distance,for which the vehicle 10 has not already stopped within a stop zonepolygon. As used in this disclosure, an “active” traffic control measuremay include traffic control measures that are in front of the vehicleand the vehicle has not already stopped. In the case of traffic lights,when assessing whether the traffic control measure is “active” adetermination may be made whether the light will be red such that thevehicle should be stopped. The high-definition map data 1771 may includepre-stored traffic light timing data.

At 438, the on-board computing device 1710 may determine if more thanone traffic control measure is found in the ROI lane segment data 1774.For example, if the road branches off into different lane segments, suchas shown in FIGS. 13, 14 and 16, one or more traffic control measuresmay be found on those branches. At 438, if the determination is “YES,”the on-board computing device 1710 may detect whether a left-turningsignal of the vehicle 10 is activated, at 440A or whether aright-turning signal of the vehicle 10 is activated, at 440B.

The turning signal direction may allow the on-board computing device1710 to determine which path the vehicle is more likely to take. Forexample, if the vehicle 10 has its left-turning signal activated, asdetermined, at 440A, the on-board computing device 1710 may ignoretraffic control measures, at 442A, which would require making a rightturn for the vehicle to reach it. On the other hand, if the vehicle 10has its right-turning signal activated, as determined, at 440B, theon-board computing device 1710 may ignore traffic control measures, at442B, which would require the vehicle to make a left turn for thevehicle to reach it.

At 440A and 440B, if the determination is “NO,” the on-board computingdevice 1710 may proceed to 444. At 444, the on-board computing device1710 may perform a search for the closest of the eligible active trafficcontrol measures, based on the distance the vehicle 10 would have totravel to reach it, as will be described in more detail in relation toFIG. 16. At 446, the on-board computing device 1710 may compute thedeceleration rate required to stop for the traffic control measure.

Referring now to FIG. 4C, at 452, the on-board computing device 1710 maydetermine whether the vehicle 10 can stop for the traffic controlmeasure under evaluation at the current deceleration rate for thestopping distance. If the determination is “YES,” the condition issatisfied and the vehicle's motion is valid. If the determination is“NO,” the condition is not satisfied and the vehicle's motion isnon-valid.

Multiple thresholds can be used for additional granularity, for example,when calculating the stopping distance and the deceleration rate. Afirst threshold may be used for “moderate deceleration,” (e.g., 2.0m/second/second (s) deceleration rate). The “moderate deceleration” mayhave a first (reasonable) deceleration jerk. A second threshold may beused for “urgent deceleration,” (e.g., 2.5 m/s/s deceleration rate). The“urgent deceleration” may have a second deceleration jerk that isdifferent from the first deceleration jerk. The second deceleration jerkmay be stronger than the first deceleration jerk. A third threshold maybe used for yellow lights beyond, which no alert is sounded. Forexample, if the vehicle would have to decelerate the vehicle's speed ata deceleration rate, which is greater than 3 m/s/s to stop the vehicle10 for a yellow light, the driver should likely just proceed through theyellow light.

In some embodiments, the driver may be provided with informationassociated with the necessary deceleration rate to meet the stoppingdistance. However, if it is determined based on the computations thatthe vehicle cannot stop for the TCM using one of the deceleration rates,the motion is non-valid because the deceleration rate is out-of-limitsto achieve a stop of the vehicle in the stop zone polygon up to the endthreshold polyline.

The on-board computing device 1710 may repeat one or more steps of themethod 400 to recalculate the stopping distance with a differentdeceleration rate before determining that vehicle 10 cannot stop for thetraffic control measure, at 452, and providing an alert, at 128 (FIG.1).

After determining whether the vehicle can stop for the traffic controlmeasure, the system may then monitor vehicle operations to determinewhen the vehicle has reached the stop threshold polyline. Once thathappens, at 454, the on-board computing device 1710 may determinewhether the vehicle has stopped in the stop zone polygon. At 454, if thedetermination is “NO,” the on-board computing device 1710 may log theevent, at 130 in FIG. 1. At 454, if the determination is “YES,” theon-board computing device 1710 may also determine whether the trafficmeasure control rules have been violated by the driver, at 456. At 456,if the determination is “YES,” the on-board computing device 1710 maylog the event, at 130 in FIG. 1. From 456, the method may return to 102of FIG. 1. At 456, if the determination is “NO,” the on-board computingdevice 1710 may also return to 102 of FIG. 1.

Example rules will now be described. In various embodiments, before thevehicle body of the vehicle 10 touches the end threshold polyline, thedriver of the vehicle 10 must stop the vehicle within the stop zonepolygon. If the vehicle 10 is not stopped before touching the endthreshold polyline, it is considered to have “run the stop sign” orviolated the traffic control measure rules. In another example rule,once the vehicle 10 has stopped in the stop zone polygon for a stop signor equivalent traffic control measure, such as a flashing red light or ared light at which it is permissible to make a right on red), the endthreshold polyline can be ignored and the vehicle 10 may proceed. Inanother example rule, once the vehicle 10 has stopped in the stop zonepolygon for a red traffic light at which it is not permitted to proceed,it must remain stopped until the light turns green. If the vehiclecrosses the end threshold polyline while the light is red, it is treatedas having violated the traffic control measure. It should be understoodthat there are many rules for different traffic control measures andshould not be limited in any way to those described above. If thevehicle 10 violates the traffic control measure, the event is logged forlater analysis, at 130.

The process for searching for the closest traffic control measureremaining, at 444, will now be described. FIG. 16 illustrates an examplescenario 1600 of a map of lane segments with distances to varioustraffic control measures. In this situation, the vehicle may detectmultiple candidate traffic control measures ahead of the vehicle in thelane in which the vehicle is traveling. If the vehicle has activated atraffic signal before reaching the first candidate TCM, the system mayuse this information to determine which TCM it should slow or stopbefore turning. For example, the system may rank the active candidatetraffic control measures based on distances from the vehicle to the nexteligible traffic signal (ranking the closest one first, the next closestone second, etc.) The system may then determine a distance between thevehicle and each of the candidate control measures, whether by measureddistance or by a number of remaining lane segments in the region ofinterest between the vehicle and the candidate traffic control measures.The system may then select the traffic control measure at which thevehicle will turn based on either or both of the distance and thevehicle turning signal state. For example, the vehicle 10 may reach atraffic control measure 1320 (i.e., stop sign) in X feet=45 feet, if itturns left onto lane segment LS16, for example. The stopping distance isbased on the lengths of each lane segment to the traffic control measure1320. The vehicle 10 may reach another traffic control measure 1310(i.e., traffic light) in Y feet=30 feet, if it continues straight. Ifthe vehicle 10 has its left-turn signal on, the traffic control measure1320 (i.e., the stop sign) will be selected as the nearest eligibleactive traffic control measure; if not, the traffic control measure 1310(i.e., traffic light) will be selected.

The methods 100, 200, 300, and 400 may be implemented using hardware,firmware, software or a combination of any of these. For instance,methods 100, 200, 300, and 400 may be implemented as part of amicrocontroller, processor, and/or graphics processing units (GPUs) andan interface with a register, data store and/or memory 1770 (FIG. 17)for storing data and programming instructions, which when executed,performs the steps of methods 100, 200, 300, and 400 described herein.

FIG. 17 illustrates an example architecture of a system 1700 with anon-board computing device 1710 for an automated vehicle. The system 1700may perform machine learning employing feature extraction algorithms fordetecting the objects and the speed of the object. The featureextraction algorithms may include, without limitation, edge detection,corner detection, template matching, dynamic texture processing,segmentation image processing, motion detection, object tracking,background subtraction, object recognition and classification, etc.

The system 1700 may control the navigation and motion of the automatedvehicle (at 126). By way of non-limiting example, the veering controlsignal may be sent to the steering controller 1724 by the on-boardcomputing device 1710.

With specific reference to FIG. 17, the system 1700 may include anengine or motor 1702 and various sensors 1735 for measuring variousparameters of the vehicle 10 and/or its environment. The system 1700 maybe integrated into a vehicle body. The automated vehicle 10 may be fullyautomated or semi-automated. Operational parameter sensors 1735 that arecommon to both types of vehicles include, for example: a position sensor1736 such as an accelerometer, gyroscope and/or inertial measurementunit; a speed sensor 1738; and an odometer sensor 1740. The system 1700also may have a clock 1742 that the system architecture uses todetermine vehicle time during operation. The clock 1742 may be encodedinto s vehicle on-board computing device 1710, it may be a separatedevice, or multiple clocks may be available.

The system 1700 also may include various sensors that operate to gatherinformation about the environment in which the vehicle is traveling.These sensors may include, for example: a location sensor 1760 such as aglobal positioning system (GPS) device; object detection sensors such asone or more cameras 1762, a laser detection and ranging (LADAR) systemand/or light detecting and ranging (LIDAR) system 1764, radio detectionand ranging (RADAR) system and/or a sound navigation and ranging (SONAR)system 1766. The object detection sensors may be part of a computervision system. The sensors 1735 also may include environmental sensors1768 such as a precipitation sensor and/or ambient temperature sensor.The object detection sensors may enable the system 1700 to detectobjects that are within a given distance or range of the vehicle 10 inany direction, while the environmental sensors collect data aboutenvironmental conditions within the vehicle's area of travel. The system1700 will also include one or more cameras 1762 for capturing images ofthe environment, such as images of TCMs as described above.

During operations, information is communicated from the sensors to anon-board computing device 1710. The on-board computing device 1710analyzes the data captured by the sensors and optionally controlsoperations of the vehicle based on results of the analysis. For example,the on-board computing device 1710 may control braking via a brakecontroller 1723; direction via a steering controller 1724; speed andacceleration via a throttle controller 1726 (in a gas-powered vehicle)or a motor speed controller 1728 (such as a current level controller inan electric vehicle); a differential gear controller 1730 (in vehicleswith transmissions); and/or other controllers such as an auxiliarydevice controller 1754. The on-board computing device 1710 may includeone or more communication links to the sensors 1735.

The system 1700 also may include a transceiver 1790 that is capable ofreceiving signals via external systems, such as V2X communications fromexternal TCMs, other vehicles, or other objects.

The on-board computing device 1710 may be implemented using hardware,firmware, software or a combination of any of these. For instance, theon-board computing device 1710 may be implemented as part of amicrocontroller, processor, and/or graphics processing units (GPUs). Theon-board computing device 1710 may include or interface with a register,data store and/or memory 1770 for storing data and programminginstructions, which when executed, performs vehicle navigation based onsensor information, such as from cameras and sensors of a computervision system.

The on-board computing device 1710 may include ROI generator 1712 forperforming the functions at 102 (FIG. 1). The ROI generator 1712 may beimplemented using hardware, firmware, software or a combination of anyof these. For instance, ROI generator 1712 may be implemented as part ofa microcontroller, processor, and/or graphics processing units (GPUs).The ROI generator 1712 may include or interface with a register, datastore or memory 1770 for storing data and programming instructions,which when executed, generates a ROI (i.e., ROI 600 (FIG. 6), 700 (FIG.7), or 1301 (FIG. 13)) of a geometric shape with a particular radius andwith the vehicle 10 in the center of the ROI. For example, the radiusmay be 50 feet to 100 feet. The radius may be in the range of 10-300feet. It should be understood, the radius may vary based on whether thevehicle is driving in the city or in a suburb. As the vehicle is driven,the ROI generator 1712 may be configured to update the ROI on ahigh-definition map.

The on-board computing device 1710 may include a map selector 1714 forperforming the functions at 104 and/or 106 (FIG. 1). The map selector1714 may be implemented using hardware, firmware, software or acombination of any of these. For instance, map selector 1714 may beimplemented as part of a microcontroller, processor, and/or graphicsprocessing units (GPUs). The map selector 1714 may include or interfacewith a register, data store or memory 1770 for storing data andprogramming instructions, which when executed, looks up a portion of themap that corresponds to the ROI (i.e., ROI 600 (FIG. 6), 700 (FIG. 7) or1301 (FIG. 13)), retrieve high-definition map data associated the ROI,and/or extract the lane segment data of the lane segments in the ROI.

The on-board computing device 1710 may include a motion monitor 1716 forperforming the functions at 108 and/or 110 (FIG. 1). The motion monitor1716 may be implemented using hardware, firmware, software or acombination of any of these. For instance, motion monitor 1716 may beimplemented as part of a microcontroller, processor, and/or graphicsprocessing units (GPUs). The motion monitor 1716 may include orinterface with a register, data store or memory 1770 for storing dataand programming instructions, which when executed, determines adirection of travel and speed of the vehicle 10 using sensed data fromsensors 1735 and/or monitors the motion of the vehicle 10 based on thelane segment data.

The on-board computing device 1710 may include a motion validator 1718for performing the function at 112 (FIG. 1). The motion validator 1718may be implemented using hardware, firmware, software or a combinationof any of these. For instance, motion validator 1718 may be implementedas part of a microcontroller, processor, and/or graphics processingunits (GPUs). The motion validator 1718 may include or interface with aregister, data store or memory 1770 for storing data and programminginstructions, which when executed, detects whether a traffic controlmeasure in in the path and/or the vehicle 10 is driving in a wrong-waydirection. The motion validator 1718 may include or interface with aregister, data store or memory 1770 for storing data and programminginstructions, which when executed, assesses valid motion associated withthe current state of the vehicle and an imminent a traffic controlmeasure in in the path identified in the high-definition map data. Themotion validator 1718 may include or interface with a register, datastore or memory 1770 for storing data and programming instructions,which when executed, assesses validity associated with the currentdriving direction of the vehicle and an imminent lane segment in thepath identified in the high-definition map data to determine whether thevehicle 10 is driving in a wrong-way direction.

The on-board computing device 1710 may include a motion corrector 1720for performing the function at 123 (FIG. 1). The motion corrector 1720may be implemented using hardware, firmware, software or a combinationof any of these. For instance, motion corrector 1720 may be implementedas part of a microcontroller, processor, and/or graphics processingunits (GPUs). The motion corrector 1720 may include or interface with aregister, data store or memory 1770 for storing data and programminginstructions, which when executed, controls the vehicle to perform amotion correction such that the vehicle returns to a valid motioncondition. In some embodiments, motion correction may include slowingthe vehicle at a deceleration rate that is within the limits to stop ina stop zone polygon. In other embodiments, the motion correction mayinclude preventing veering or steering of the vehicle or causing avehicle to make a safe U-turn. Other motion corrections are contemplatedto safely correct the motion of the vehicle until it returns to a validmotion condition associated with wrong-way directions and stoppingdistances to traffic control measures.

The on-board computing device 1710 may include an alert generator 1721for performing the function at 128 (FIG. 1). The alert generator 1721may be implemented using hardware, firmware, software or a combinationof any of these. For instance, alert generator 1721 may be implementedas part of a microcontroller, processor, and/or graphics processingunits (GPUs). The alert generator 1721 may include or interface with aregister, data store or memory 1770 for storing data and programminginstructions, which when executed, generates a control signal to causethe driver warning system 1780 to generate an alert using one or more ofan audible alert, a visual alert or a vibration alert representative ofthe vehicle traveling in a wrong-way direction and/or to an imminenttraffic control measure with an out-of-limit deceleration rate.

The on-board computing device 1710 may include a logging module 1722 forperforming the function, at 130 (FIG. 1). The logging module 1722 may beimplemented using hardware, firmware, software or a combination of anyof these. For instance, logging module 1722 may be implemented as partof a microcontroller, processor, and/or graphics processing units(GPUs). The logging module 1722 may include or interface with aregister, data store or memory 1770 for storing data and programminginstructions, which when executed, logs the event data or informationassociated with alerts. The event data corresponds to the vehicle'ssensor data from sensors 1735, current lane segment data, trafficcontrol measure data, and non-valid motion detection data. The eventdata may include logging of violation of rules associated with trafficcontrol measures and/or stopping locations by the vehicle associatedwith the stop zone polygon. The event data or information may includeimages captures in the area representative of poor signage.

The on-board computing device 1710 may perform machine learning forplanning the motion of the vehicle along a route from an originationlocation to a destination location of global coordinate system. Theparameter may include, without limitation, motor vehicle operation lawsof a jurisdiction (i.e., speed limits), objects in a path of thevehicle, scheduled or planned route, traffic lights of intersections,and/or the like.

Geographic location information may be communicated from the locationsensor 1760 to the on-board computing device 1710, which may then accessa map of the environment that corresponds to the location information todetermine known fixed features of the environment such as streets,buildings, stop signs and/or stop/go signals. The map includes map data1771. In addition or alternatively, the vehicle 10 may transmit any ofthe data to a remote server system (not shown) for processing. Any knownor to be known technique for making an object detection based on sensordata and/or captured images can be used in the embodiments disclosed inthis document.

To determine a heading (i.e., sensed direction of travel) of theautomated vehicle, the on-board computing device 1710 may determine thelocation, orientation, pose, etc. of the automated vehicle in theenvironment (localization) based on, for example, three dimensionalposition data (e.g., data from a GPS), three dimensional orientationdata, predicted locations, or the like. For example, the on-boardcomputing device 1710 may receive GPS data to determine the automatedvehicle's latitude, longitude and/or altitude position. Other locationsensors or systems such as laser-based localization systems,inertial-aided GPS, or camera-based localization may also be used toidentify the location of the vehicle. The location of the vehicle 10 mayinclude an absolute geographical location, such as latitude, longitude,and altitude as well as relative location information, such as locationrelative to other cars immediately around it, which can often bedetermined with less noise than absolute geographical location. The mapdata 1774 can provide information regarding: the identity and locationof different roadways, lane segments, buildings, or other items, thelocation, boundaries, and directions of traffic lanes (e.g., thelocation and direction of a parking lane, a turning lane, a bicyclelane, or other lanes within a particular roadway) and metadataassociated with traffic lanes, traffic control data (e.g., the locationand instructions of signage, traffic lights, or other traffic controlmeasures); and/or any other map data 1774 that provides information thatassists the on-board computing device 1710 in analyzing the surroundingenvironment of the automated vehicle 10. The map data 1774 may alsoinclude information and/or rules for determining right of way of objectsand/or vehicles in conflicted areas or spaces.

In certain embodiments, the map data 1774 may also include referencepath information that correspond to common patterns of vehicle travelalong one or more lanes such that the motion of the object isconstrained to the reference path (e.g., locations within traffic laneson which an object commonly travels). Such reference paths may bepre-defined such as the centerline of the traffic lanes. Optionally, thereference path may be generated based on a historical observations ofvehicles or other objects over a period of time (e.g., reference pathsfor straight line travel, lane merge, a turn, or the like).

In various implementations, an on-board computing device 1710 maydetermine perception information of the surrounding environment of theautomated vehicle 10. Based on the sensor data provided by one or moresensors and location information that is obtained, the on-boardcomputing device 1710 may determine perception information of thesurrounding environment of the automated vehicle 10. The perceptioninformation may represent what an ordinary driver would perceive in thesurrounding environment of a vehicle. The perception data may includeinformation relating to one or more objects in the environment of theautomated vehicle 10. For example, the on-board computing device 1710may process perception data that includes sensor data (e.g., LADAR data,LIDAR data, RADAR data, SONAR data, camera images, etc.) in order toidentify objects and/or features in the environment of automated vehicle10. The objects may include traffic signals, road way boundaries, othervehicles, pedestrians, and/or obstacles, etc. The on-board computingdevice 1710 may use any now or hereafter known object recognitionalgorithms, video tracking algorithms, and computer vision algorithms(e.g., track objects frame-to-frame iteratively over a number of timeperiods) to determine the perception. The perception information mayinclude objects identified by discarding ground LIDAR point, asdiscussed below.

In the various embodiments discussed in this document, the descriptionmay state that the vehicle or a controller included in the vehicle(e.g., in an on-board computing system) may implement programminginstructions that cause the vehicle and/or a controller to makedecisions and use the decisions to control operations of the vehicle.However, the embodiments are not limited to this arrangement, as invarious embodiments the analysis, decision making and or operationalcontrol may be handled in full or in part by other computing devicesthat are in electronic communication with the vehicle's on-boardcomputing device and/or vehicle control system. Examples of such othercomputing devices include an electronic device (such as a smartphone)associated with a person who is riding in the vehicle, as well as aremote server that is in electronic communication with the vehicle via awireless communication network. The processor of any such device mayperform the operations that will be discussed below.

The above-disclosed features and functions, as well as alternatives, maybe combined into many other different systems or applications. Variouscomponents may be implemented in hardware or software or embeddedsoftware. Various presently unforeseen or unanticipated alternatives,modifications, variations or improvements may be made by those skilledin the art, each of which is also intended to be encompassed by thedisclosed embodiments.

Terminology that is relevant to the disclosure provided above includes:

The term “vehicle” refers to any moving form of conveyance that iscapable of carrying either one or more human occupants and/or cargo andis powered by any form of energy. The term “vehicle” includes, but isnot limited to, cars, trucks, vans, trains, automated vehicles,aircraft, aerial drones and the like. An “automated vehicle” is avehicle having a processor, programming instructions and drivetraincomponents that are controllable by the processor without requiring ahuman operator. An automated vehicle may be fully automated in that itdoes not require a human operator for most or all driving conditions andfunctions. Alternatively, it may be semi-automated in that a humanoperator may be required in certain conditions or for certainoperations, or that a human operator may override the vehicle'sautomated system and may take control of the vehicle. Automated vehiclesalso include vehicles in which automated systems augment human operationof the vehicle, such as vehicles with driver-assisted steering, speedcontrol, braking, parking and other advanced driver assistance systems.

An “electronic device” or a “computing device” refers to a device thatincludes a processor and memory. Each device may have its own processorand/or memory, or the processor and/or memory may be conflicted withother devices as in a virtual machine or container arrangement. Thememory will contain or receive programming instructions that, whenexecuted by the processor, cause the electronic device to perform one ormore operations according to the programming instructions.

The terms “memory,” “memory device,” “data store,” “data storagefacility” and the like each refer to a non-transitory computer-readablemedium where programming instructions and data are stored. Except wherespecifically stated otherwise, the terms “memory,” “memory device,”“data store,” “data storage facility” and the like are intended toinclude single device embodiments, embodiments in which multiple memorydevices together or collectively store a set of data or instructions, aswell as individual sectors within such devices.

The terms “processor” and “processing device” refer to a hardwarecomponent of an electronic device that is configured to executeprogramming instructions. Except where specifically stated otherwise,the singular term “processor” or “processing device” is intended toinclude both single-processing device embodiments and embodiments inwhich multiple processing devices together or collectively perform aprocess.

In this document, the terms “communication link” and “communicationpath” mean a wired or wireless path via which a first device sendscommunication signals to and/or receives communication signals from oneor more other devices. Devices are “communicatively connected” if thedevices are able to send and/or receive data via a communication link.“Electronic communication” refers to the transmission of data via one ormore signals between two or more electronic devices, whether through awired or wireless network, and whether directly or indirectly via one ormore intermediary devices.

When used in the context of autonomous vehicle motion planning, the term“trajectory” refers to the plan that the vehicle's motion planningsystem will generate, and which the vehicle's motion control system willfollow when controlling the vehicle's motion. A trajectory includes thevehicle's planned position and orientation at multiple points in timeover a time horizon, as well as the vehicle's planned steering wheelangle and angle rate over the same time horizon. An autonomous vehicle'smotion control system will consume the trajectory and send commands tothe vehicle's steering controller, brake controller, throttle controllerand/or other motion control subsystem to move the vehicle along aplanned path.

The term “classifier” means an automated process by which an artificialintelligence system may assign a label or category to one or more datapoints. A classifier includes an algorithm that is trained via anautomated process such as machine learning. A classifier typicallystarts with a set of labeled or unlabeled training data and applies oneor more algorithms to detect one or more features and/or patterns withindata that correspond to various labels or classes. The algorithms mayinclude, without limitation, those as simple as decision trees, ascomplex as Naïve Bayes classification, and/or intermediate algorithmssuch as k-nearest neighbor. Classifiers may include artificial neuralnetworks (ANNs), support vector machine classifiers, and/or any of ahost of different types of classifiers. Once trained, the classifier maythen classify new data points using the knowledge base that it learnedduring training. The process of training a classifier can evolve overtime, as classifiers may be periodically trained on updated data, andthey may learn from being provided information about data that they mayhave mis-classified. A classifier will be implemented by a processorexecuting programming instructions, and it may operate on large datasets such as image data, LADAR system data, LIDAR system data, and/orother data.

The term “object,” when referring to an object that is detected by avehicle perception system or simulated by a simulation system, isintended to encompass both stationary objects and moving (or potentiallymoving) actors or pedestrians, except where specifically statedotherwise by terms use of the term “actor” or “stationary object.”

In this document, when relative terms of order such as “first” and“second” are used to modify a noun, such use is simply intended todistinguish one item from another, and is not intended to require asequential order unless specifically stated.

In addition, terms of relative position such as “front” and “rear”, whenused, are intended to be relative to each other and need not beabsolute, and only refer to one possible position of the deviceassociated with those terms depending on the device's orientation. Whenthis document uses the terms “front,” “rear,” and “sides” to refer to anarea of a vehicle, they refer to areas of vehicle with respect to thevehicle's default area of travel. For example, a “front” of anautomobile is an area that is closer to the vehicle's headlamps than itis to the vehicle's tail lights, while the “rear” of an automobile is anarea that is closer to the vehicle's tail lights than it is to thevehicle's headlamps. In addition, the terms “front” and “rear” are notnecessarily limited to forward-facing or rear-facing areas but alsoinclude side areas that are closer to the front than the rear, or viceversa, respectively. “Sides” of a vehicle are intended to refer toside-facing sections that are between the foremost and rearmost portionsof the vehicle.

1. A method of assisting a driver of a vehicle, the method comprising byone or more sensors of a vehicle, sensing a direction of travel of thevehicle; and by a computing device of the vehicle: identifying a regionof interest comprising an area in proximity to and including a currentlocation of the vehicle, accessing a vector map that includes the regionof interest, extracting lane segment data associated with lane segmentsof the vector map that are within the region of interest, analyzing thelane segment data and the direction of travel to determine whethermotion of the vehicle satisfies a condition associated with one or moreof the following: a direction of travel of a lane that corresponds tothe current location of the vehicle, or a minimum stopping distance toan imminent traffic control measure in the lane that corresponds to thecurrent location of the vehicle, and when the motion does not satisfythe condition, controlling a driver warning system of the vehicle togenerate and output a driver alert of non-valid motion.
 2. The method ofclaim 1, wherein: the lane segment data includes a heading for each lanesegment in the region of interest, wherein the heading corresponds tothe direction of travel of the lane that corresponds to the currentlocation of the vehicle; the condition is associated with the directionof travel of the lane that corresponds to the current location of thevehicle; and determining whether the motion of the vehicle satisfies thecondition comprises, by the computing device of the vehicle: (a)determining whether any of the lane segments within the region ofinterest has a heading that is within an opposing-heading tolerancerange and that is opposing the sensed direction of travel, (b)determining whether any of the lane segments within the region ofinterest has a heading that is within a similar-heading tolerance rangeand that is similar to the sensed direction of travel, and (c)determining that the motion does not satisfy the condition, and that thesensed direction of travel is a wrong-way direction, when both: (i) atleast one of the lane segments is determined at (a) to have the headingopposing the sensed direction of travel, and (ii) none of the lanesegments in the region of interest is determined at (b) have the headingthat is similar to the sensed direction of travel, otherwise determiningthat the satisfies the condition.
 3. The method of claim 1, wherein: thelane segment data includes a heading and length for each lane segment;the condition is associated with the minimum stopping distance to theimminent traffic control measure in the lane that corresponds to thecurrent location of the vehicle; and determining whether the motion ofthe vehicle satisfies the condition comprises, by the computing deviceof the vehicle: receiving, from one or more sensors of the vehicle, aspeed of travel of the vehicle, determining a minimum stopping distancerepresenting a distance from the current location of the vehicle and alocation within a stop zone that is within the lane of travel before thelocation of the imminent traffic control measure, and using the speedand the current location to determine whether the vehicle can stopwithin the minimum stopping distance.
 4. The method of claim 3, wherein:using the speed and the current location to determine whether thevehicle can stop within the minimum stopping distance comprises, by thecomputing device of the vehicle: computing a rate of decelerationrequired to stop the vehicle at the location within the stop zone,determining if the computed rate of deceleration meets of a decelerationthreshold, and if the computed rate of deceleration is over thedeceleration threshold, determining that the motion does not satisfy thecondition, otherwise determining that the motion satisfies thecondition.
 5. The method of claim 3, wherein the determining whether themotion of the vehicle satisfied the condition, further comprises, by thecomputing device of the vehicle: prior to determining the minimumstopping distance, calculating a stopping zone polygon for the imminenttraffic control measure; and calculating an end threshold polyline forthe imminent traffic control measure that is at an end of the stoppingzone polygon, wherein the location within the stop zone corresponds tothe end threshold polyline, and the minimum stopping distance isdetermined through the stopping zone polygon and up to the end thresholdpolyline.
 6. The method of claim 5, wherein: calculating the stoppingzone polygon comprises setting a width of the stopping zone polygon tobe larger than a width of the lane segment; and calculating the minimumstopping distance comprises including, in the minimum stopping distance,a distance of travel that includes a veering motion to an end of thewidth of the stopping zone polygon.
 7. The method of claim 1, whereindetermining whether the motion of the vehicle satisfies the conditionfurther comprises, by the computing device of the vehicle: detecting aplurality of candidate traffic control measures ahead of the vehicle inthe direction of travel of the vehicle; detecting that a traffic signalactivation command has been initiated in the vehicle before the vehiclereaches a first one of the candidate traffic control measures; rankingthe candidate traffic control measures based on distances from thevehicle to each of the candidate control measures or remaining lanesegments in the region of interest between the vehicle and the candidatetraffic control measures; and selecting the imminent traffic controlmeasure based on one or both of the distance and the vehicle turningsignal state.
 8. A system for assisting a driver of a vehicle, thesystem comprising a vehicle having one or more sensors, a driver warningsystem, and an onboard computing system that comprises a processor, anda memory portion containing programming instructions that, whenexecuted, will cause the processor to: determine a direction of travelof the vehicle, identify a region of interest comprising an area inproximity to and including a current location of the vehicle, access avector map that includes the region of interest, extract lane segmentdata associated with lane segments of the vector map that are within theregion of interest, analyze the lane segment data and the direction oftravel to determine whether motion of the vehicle satisfies a conditionassociated with one or more of the following: a direction of travel of alane that corresponds to the current location of the vehicle, or aminimum stopping distance to an imminent traffic control measure in thelane that corresponds to the current location of the vehicle, and whenthe motion does not satisfy the condition, control the driver warningsystem of the automated vehicle to generate and output a driver alert ofnon-valid motion.
 9. The system of claim 8, wherein: the lane segmentdata includes a heading for each lane segment in the region of interest,wherein the heading corresponds to the direction of travel of the lanethat corresponds to the current location of the vehicle; and theinstructions to determine whether the motion of the vehicle satisfiesthe condition comprise instructions to: (a) determine whether any of thelane segments within the region of interest has a heading that is withinan opposing-heading tolerance range and that is opposing to the senseddirection of travel, (b) determine whether any of the lane segmentswithin the region of interest has a heading that is within asimilar-heading tolerance range and that is similar to the senseddirection of travel, and (c) determine that the motion does not satisfythe condition, and that the sensed direction of travel of the vehicle isa wrong-way direction when both: (i) at least one the lane segments isdetermined at (a) to have the heading opposing the sensed direction oftravel, and (ii) none of the lane segments in the region of interest isdetermined at (b) have the heading that is similar to the senseddirection of travel, otherwise determine that the motion satisfies thecondition.
 10. The system of claim 8, wherein: the lane segment dataincludes a heading and length for each lane segment; the condition isassociated with the minimum stopping distance to the imminent trafficcontrol measure in the lane that corresponds to the current location ofthe vehicle; and the instructions to determine whether the motion of thevehicle satisfies the condition comprise instructions to: receive, fromone or more of the sensors, a speed of travel of the vehicle, determinea minimum stopping distance representing a distance from the currentlocation of the vehicle and a location within a stop zone that is withinthe lane of travel before the location of the imminent traffic controlmeasure, and using the speed and the current location to determinewhether the vehicle can stop within the minimum stopping distance. 11.The system of claim 10, wherein the instructions to use the speed andthe current location to determine whether the vehicle can stop withinthe minimum stopping distance comprise instructions to: compute a rateof deceleration required to stop the vehicle at the location within thestop zone; determine if the computed rate of deceleration meets adeceleration threshold; and if the computed rate of deceleration is overthe deceleration threshold, determine that the motion does not satisfythe condition, otherwise determine that the motion satisfies thecondition.
 12. The system of claim 11, wherein the instructions todetermine whether the motion of the automated vehicle satisfies thecondition further comprise instructions to, prior to determining theminimum stopping distance: calculate a stopping zone polygon for theimminent traffic control measure; and calculate an end thresholdpolyline for the imminent traffic control measure that is at an end ofthe stopping zone polygon, wherein the location within the stop zonecorresponds to the end threshold polyline, and the minimum stoppingdistance is determined to the stopping zone polygon and up to the endthreshold polyline.
 13. The system of claim 12, wherein: theinstructions to calculate the stopping zone polygon compriseinstructions to set a width of the stopping zone polygon to be largerthan a width of the lane segment; and the instructions to calculate theminimum stopping distance comprise instructions to calculate the minimumstopping distance to include a distance of travel that includes aveering motion to an end of the width of the stopping zone polygon. 14.The system of claim 8, wherein the instructions to determine whether themotion of the vehicle satisfies the condition further compriseinstructions to: detect a plurality of candidate traffic controlmeasures ahead of the vehicle in the direction of travel of the vehicle;detect that a traffic signal activation command has been initiated inthe vehicle before the vehicle reaches a first one of the plurality ofcandidate traffic control measures; rank the candidate traffic controlmeasures based on distances from the vehicle to each of the candidatetraffic control measures or remaining lane segments in the region ofinterest between the vehicle and the candidate traffic control measures;and select the imminent traffic control measure based on one or both ofthe distance and the vehicle turning signal state.
 15. A method ofdetermining whether an automated vehicle is moving in a wrong-waydirection in a lane, the method comprising: by an electronic device ofthe automated vehicle: (a) calculating a location, orientation, pose ofthe automated vehicle, (b) determining a direction of travel for theautomated vehicle based on the calculated location, orientation, pose ofthe automated vehicle, (c) identifying a region of interest comprisingan area in an immediate vicinity of the location of the automatedvehicle, (d) accessing a vector map that includes the region ofinterest, and extracting from the vector map a plurality of lanesegments that are within the region of interest, wherein each of thelane segments includes a heading that associates the lane segment with alane's direction of travel, (e) determining whether any of the lanesegments within the region of interest has a heading within an opposingheading tolerance range opposing to the determined direction of travel,(f) determining whether any of the lane segments within the region ofinterest near the location of the automated vehicle has a heading withina similar heading tolerance range that is similar to the senseddirection of travel, and (g) determining the automated vehicle has amotion that is a wrong-way direction when both: (i) at least one thelane segments is determined at (e) to have the heading opposing thedetermined direction of travel, and (ii) none of the lane segments at(f) have the heading that is similar to the determined direction oftravel.
 16. The method of claim 15, further comprising, by theelectronic device of the automated vehicle, in response to determiningthat the motion is the wrong-way direction, generating a control signalto cause an alert to inform a human operator that the direction oftravel for the automated vehicle is in the wrong-way direction.
 17. Themethod of claim 16, further comprising, by the electronic device of theautomated vehicle, in response to determining that the motion is thewrong-way direction, logging information associated with determiningthat the motion is the wrong-way direction.
 18. The method of claim 15,wherein the identifying the region of interest comprises, by theelectronic device of the automated vehicle: determining a trajectory ofthe automated vehicle; identifying a radius around the automatedvehicle; and defining the region of interest as a circle in which theautomated vehicle is positioned at a center point of the circle.
 19. Themethod of claim 15, further comprising, in response to determining thatthe direction of travel for the automated vehicle is the wrong-waydirection, correcting the motion of the automated vehicle.
 20. Themethod of claim 19, wherein the correcting the motion comprises, by theelectronic device of the automated vehicle, generating a control signalto control at least one of a steering controller, a speed controller anda brake controller of the automated vehicle.